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                    <h1 class="description center-align post-title">Elasticsearch的跨索引查询详细讲解(转载)</h1>
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                    <i class="far fa-calendar-minus fa-fw"></i>发布日期:&nbsp;&nbsp;
                    2021-04-08
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                <h4 id="序言"><a href="#序言" class="headerlink" title="序言"></a>序言</h4><p>Elasticsearch，中文名直译弹性搜索，不仅仅在单索引内部分片层面弹性搜索，更强的是在跨索引外围支持分片弹性搜索，同比其它分布式数据产品，此特性更鲜明，代表了 Elastic 集群架构设计的优越性。</p>
<p>本文将从以下几个方面展开探讨：</p>
<ul>
<li>为什么需要跨索引查询？</li>
<li>跨索查询有哪些经典应用场景？</li>
<li>跨索引查询技术原理是怎样的？</li>
<li>跨索引查询有哪些注意事项？</li>
</ul>
<p><img src="" alt="图示：跨索引示意图 + 多个索引查询效果图"></p>
<h4 id="为什么需要跨索引查询"><a href="#为什么需要跨索引查询" class="headerlink" title="为什么需要跨索引查询"></a>为什么需要跨索引查询</h4><h5 id="技术限制"><a href="#技术限制" class="headerlink" title="技术限制"></a>技术限制</h5><p>Elasticsearch 索引本身有一些指标限制，对于很多新手来说最容易忽视或者乱用。</p>
<ul>
<li>Elastic 索引数据量有大小限制；</li>
<li>单个分片数据容量官方建议不超过 50GB，合理范围是 20GB～40GB 之间；</li>
<li>单个分片数据条数不超过约 21 亿条（2 的 32 次方），此值一般很难达到，基本可以忽略，背后原理可以参考源码或者其它；</li>
<li>索引分片过多，分布式资源消耗越大，查询响应越慢。</li>
</ul>
<p>基于以上限制，索引在创建之前就需要依据业务场景估算，设置合理的分片数，不能过多也不能过少。</p>
<h5 id="技术便利"><a href="#技术便利" class="headerlink" title="技术便利"></a>技术便利</h5><p>在基于关系型数据库的应用场景中，数据量过大，一般会采用分库分表策略，查询数据时基于第三方中间件，限制多多；在基于 NoSQL 的应用场景中，如 MongoDB，数据量过大，会采用数据产品本身提供的分片特性，查询数据时基于自身的路由机制。</p>
<p>无论是分库分表还是分片，它们只解决了一维数据的存储与查询，二维的不能，如电商订单系统场景，数据库采用多库多表拆分，一旦容量超过预期设计，需要二次拆分继续分库分表；MongoDB 采用多分片拆分，一旦容量超过预计设计，需要继续扩展分片节点。</p>
<p>以上对于 Elasticsearch 可以不用这样，它提供了两个维度的拆分方式，第一维度采用多个索引命名拆分，第二维度采用索引多分片，对于查询来说，可以灵活匹配索引，一次指定一个索引，也可以一次指定多个索引。</p>
<p><img src="" alt="图示：ES 查询示意图 + 多索引 + 多分片示意图"></p>
<h5 id="跨索引查询应用场景"><a href="#跨索引查询应用场景" class="headerlink" title="跨索引查询应用场景"></a>跨索引查询应用场景</h5><p>IT 应用中，除去技术本身局限问题，多数的问题都是由于耦合造成的，“高内聚，低耦合”一直是我们 IT 从业者的座右铭。应用系统耦合，就成了单体应用，然后就延伸出微服务架构理念。同样数据耦合，我们也要基于一定维度的微服务化，或垂直或水平或混合垂直水平。</p>
<h5 id="业务系统"><a href="#业务系统" class="headerlink" title="业务系统"></a>业务系统</h5><p>举例某些业务场景，实时数据与历史数据存储和查询问题，假设日均数据量超过千万条，那么月度数量超过 3 亿条，年度也会超过 36 亿条。</p>
<p>若采用 Elasticsearch 存储，则可以按月 / 按季度 / 按年度 创建索引，这样实时数据的更新只会影响当前的索引，不影响历史的索引；查询时也一样，依据查询条件指定索引名称，按需要扫描查询，无需每次扫描所有的数据。这比基于传统的数据产品灵活很多。</p>
<p><img src="" alt="图示：实时数据与历史数据业务场景"></p>
<h5 id="大数据"><a href="#大数据" class="headerlink" title="大数据"></a>大数据</h5><p>Elasticsearch 在大数据应用场景下很受欢迎，已经成为大数据平台对外提供结果查询的标配。大数据平台需要定期计算数据，将结果数据批量写入到 Elasticsearch 中，供业务系统查询，由于部分业务规则设定，Elasticsearch 原来的索引数据要全部删除，并重新写入，这种操作很频繁。对于大数据平台每次全量计算，代价很大，对于 Elasticsearch 平台，超大索引数据频繁删除重建，代价也很大。</p>
<p>基于以上，采用多索引方式，如按照月份拆解，依据需要删除的月份索引数据。同样的问题，业务系统查询时，非常灵活指定需要的月份索引数据，这样保证了存储与查询的平衡。</p>
<p><img src="" alt="图示：大数据平台写数据到 Elastic 平台示意图"></p>
<h5 id="日志"><a href="#日志" class="headerlink" title="日志"></a>日志</h5><p>Elasticsearch 应对这个日志场景非常擅长，诞生了著名的 ELK 组合，比如一个大中型的业务系统，每天日志量几十 TB/ 几百 TB 很正常，可按天或者按小时或者更小粒度创建索引，通常查询日志只会查询最近时间的，过去很久的日志，偶然需要查询几次，甚至会删除。所以对于此场景，Elasticsearch 的跨索引查询非常便利，程序编写也很简单。</p>
<h5 id="跨索引查询应用方式"><a href="#跨索引查询应用方式" class="headerlink" title="跨索引查询应用方式"></a>跨索引查询应用方式</h5><p>Elasticsearch 跨索引查询的方式可依据业务场景灵活选择，下面介绍几种：</p>
<h5 id="直接型"><a href="#直接型" class="headerlink" title="直接型"></a>直接型</h5><p>明确指定多个索引名称，这种方式一般应用在非常精确的查询场景下，便于查询索引范围，性能平衡考虑，若索引不存在会出现错误，如下：index_01,index_02</p>
<pre><code>GET /index_01,index_02/_search
&#123;
    &quot;query&quot; : &#123;
        &quot;match&quot;: &#123;
        &quot;test&quot;: &quot;data&quot;
        &#125;
    &#125;
&#125;
</code></pre>
<h5 id="模糊型"><a href="#模糊型" class="headerlink" title="模糊型"></a>模糊型</h5><p>不限定死索引名称，这种方式一般采用通配符，无需判断该索引是否存在，支持前匹配、后匹配，前后匹配，如下：index_* 匹配前缀一样的所有索引</p>
<pre><code>GET /index_*/_search
&#123;
    &quot;query&quot; : &#123;
        &quot;match&quot;: &#123;
        &quot;test&quot;: &quot;data&quot;
        &#125;
    &#125;
&#125;
</code></pre>
<h5 id="计算型"><a href="#计算型" class="headerlink" title="计算型"></a>计算型</h5><p>索引名称通过计算表达式指定，类似正则表达式，也可以同时指定多个索引，如下：logstash-{now/d}表示当前日期</p>
<pre><code># 索引名称如：index-2024.03.22
# GET /&lt;index-&#123;now/d&#125;&gt;/_search
GET /%3Cindex-%7Bnow%2Fd%7D%3E/_search&#123;
    &quot;query&quot; : &#123;
        &quot;match&quot;: &#123;
        &quot;test&quot;: &quot;data&quot;
        &#125;
    &#125;
&#125;
</code></pre>
<h5 id="跨索引查询技术原理"><a href="#跨索引查询技术原理" class="headerlink" title="跨索引查询技术原理"></a>跨索引查询技术原理</h5><p>Elasticsearch 能够做到跨索引查询，离不开其架构设计以及相关实现原理。</p>
<h5 id="索引分片"><a href="#索引分片" class="headerlink" title="索引分片"></a>索引分片</h5><p><img src="" alt="图示：索引由分片组成"></p>
<ul>
<li>索引是一个虚拟的数据集合，索引由多个分片组成；</li>
<li>分片存储实际的数据；</li>
<li>索引分片数量不限制。</li>
</ul>
<h5 id="查询过程"><a href="#查询过程" class="headerlink" title="查询过程"></a>查询过程</h5><p><img src="" alt="图示：索引查询阶段"></p>
<p><img src="" alt="图示：取回数据阶段"></p>
<p>查询过程简单说来就是分发与合并：</p>
<ul>
<li>查询分发，客户端发送请求到协调节点，协调节点分发查询请求到索引分片节点；</li>
<li>数据合并，索引分片节点将数据发送到协调节点，协调节点合并返回客户端。</li>
</ul>
<p>所以说，Elasticsearch 提供跨索引查询的能力，实际上与原来单索引查询时一样，本质上是跨多个分片查询，然后合并。</p>
<h5 id="跨索引查询注意事项"><a href="#跨索引查询注意事项" class="headerlink" title="跨索引查询注意事项"></a>跨索引查询注意事项</h5><h5 id="索引与分片等价关系"><a href="#索引与分片等价关系" class="headerlink" title="索引与分片等价关系"></a>索引与分片等价关系</h5><p>索引与分片等价的关系，1 个索引 20 分片与 4 个索引每个索引 5 个分片理论上是等价的，鉴于索引分片的容量限制与性能平衡，在面对需要跨索引业务场景时，索引的数量与分片的数量尽量的少，既要保障索引热点数据的实时处理能力，也要平衡历史数据的查询性能。</p>
<h5 id="协调节点分离"><a href="#协调节点分离" class="headerlink" title="协调节点分离"></a>协调节点分离</h5><p>鉴于 Elastic 查询过程，在跨多个索引查询时，协调节点承担了所有分片查询返回的数据合并，需要消耗很大资源，在应对高并发场景，建议部署独立的协调节点，将集群的数据节点与协调节点分离，以达到最佳的性能平衡。</p>
<h5 id="路由机制"><a href="#路由机制" class="headerlink" title="路由机制"></a>路由机制</h5><p>Elasticsearch 写入数据分布默认是基于索引主键 _id 的 Hash 值，此机制在数据分布上很均衡，但也没有什么规律，对于跨索引查询场景，若自定义指定路由键，可以在搜索时避开不需要的索引分片，有效减少分片查询的分片数量，达到更高的性能。</p>
<h4 id="总结"><a href="#总结" class="headerlink" title="总结"></a>总结</h4><p>Elasticsearch 由于其架构设计的弹性能力，小小的一个跨索引查询特性，就能给我们应用系统带来很多架构设计的便利，解决很多实际场景问题，这是其它数据产品目前还做不到的。Elasticsearch 还有更厉害的跨多个集群跨多个版本，详情可继续关注笔者下一篇文章的探讨。</p>
<p>原文：<a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/145021757">https://zhuanlan.zhihu.com/p/145021757</a></p>

                
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